FME for HPMS Reporting Challenges

Whether you are glad that the HPMS season will be ending soon, or stressed that the deadline is looming, or both, you owe it to yourself to check out FME-based tools for HPMS.

Why not? At its core, FME provides a comprehensive set of data ETL tools that extend beyond the spatial domain. Moreover, it provides a platform to design, test, develop and document your workflow which is highly adaptable to changes in requirements as well as data sources.

At the GIS-T 2016 Symposium in Raleigh NC, Dave Campanas of Safe Software and I jointly presented FME & ARNOLD: Superman to the Rescue! After the session, Kyle Konterwitz, GIS Manager of Kansas DOT, approached me for generating a report using FME, something they had attempted for some time now – a project feature report segmented by Functional Classification and NHS designation, as well as several administrative and political boundaries.

At first glance, this commonly-requested report is conceptually simple. A deeper look into the requirements and data sets resulted in the following multi-step process:

Merge HPMS segments based on functional classification and NHS code

Join (overlay) project events with the events resulting from Step 1

LRS geocode the events resulting from Step 2 to turn it into a feature dataset

Overlay line features from Step 3 with boundary features to get the attributes from the boundaries

LRS reverse geocode the result from Step 4 so each feature will have the correct From Measure and To Measure values in its attributes

Optionally, remove sliver project segments as a result of discrepancies between the data layers

Out of the box, FME does not provide a direct solution. With the help of LinearBench® custom transformers such as LRS_EventMerger, LRS_EventJoiner, LRS_Geocoder, and LRS_RevGeoCoder, the process was made clean, friendly and adaptable to changes.The second challenge came from Dave Blackstone, GIS Manager of Ohio DOT, who would like to summarize a subject event data set over a reference data set for key statistics, including length-predominate stats, among other things. This capability is already implemented in LinearBench® Analyze; still we decided to also offer it as an FME custom transformer, and LRS_EventSummarizer was born. The following workspace shows the simple workflow for this challenge:

Partial result of the report is shown in Table 1.

Table 1 UDOT’s AADT Summary Statistics over Speed Limit Segments

ID

Route_ID

Speed_
Limit

F_Meas

T_Meas

AADT_Min

AADT_Max

AADT_
Count

AADT_
Mean

AADT_length_
Predominant

AADT_length_
Weighted

3940

252

40

0.374

1.311

18701

23436

2

21069

18701

18918

3939

252

45

1.311

2.292

22837

23436

2

23137

23436

23418

4104

254

45

0.142

0.671

789

789

1

789

789

788

4103

254

35

0.671

0.672

789

789

1

789

789

789

4102

254

45

0.672

1.333

789

789

1

789

789

789

4319

255

30

0.061

0.689

465

619

2

542

465

509

4318

255

65

0.689

65.635

407

832

3

619

832

740

4317

255

40

65.635

66.774

407

1332

2

870

407

860

4316

255

55

66.774

69.094

1332

1332

1

1332

1332

1332

4327

256

55

0.195

6.819

113

130

2

122

113

114

4326

256

30

6.819

7.144

113

113

1

113

113

113

4325

256

15

7.144

7.308

113

113

1

113

113

113

4324

256

5

7.308

9.68

92

113

2

103

113

112

4323

256

55

9.68

9.784

92

92

1

92

92

91

4322

256

15

9.784

10.22

92

92

1

92

92

92

4321

256

30

10.22

14.967

92

92

1

92

92

92

4320

256

55

14.967

32.491

92

102

2

97

102

97

21

257

40

0.22

0.475

12978

12978

1

12978

12978

12978

20

257

35

0.475

0.544

12978

12978

1

12978

12978

12977

While the 2015 HPMS season will end shortly, knowing FME is there to help you with future HPMS challenges may just make the off season more enjoyable!